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Una nueva y eficiente técnica de detección de estrés personalizada que utiliza un modelo de aprendizaje profundo

Ulligaddala Srinivasarao1, Gopisetty Rathnamma2, M Satish Kumar3

  • 1Department of CSE, GITAM (Deemed to be) University, Rudraram Village, Hyderabad, India. ulligaddalasrinu@gmail.com.

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Resumen

Este estudio introduce un método eficiente para detectar el estrés de los textos de las redes sociales. El nuevo enfoque integra representaciones de texto avanzadas con un modelo de aprendizaje profundo, logrando una alta precisión en la detección de estrés.

Palabras clave:
DeepMoji también.Texto rápidoOptimización de FoxRed de residuosEl descensoDetección del estrésLa tokenización

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Área de la Ciencia:

  • Lingüística computacional
  • Inteligencia artificial
  • Psicología

Sus antecedentes:

  • El estrés afecta significativamente la salud de los adultos y los ancianos, lo que lleva a enfermedades crónicas.
  • La detección de estrés a partir del texto de las redes sociales presenta desafíos debido a la complejidad y las demandas computacionales.
  • Los modelos existentes de aprendizaje automático y aprendizaje profundo se enfrentan a limitaciones como largos tiempos de capacitación y limitaciones de características.

Objetivo del estudio:

  • Desarrollar una técnica eficiente y precisa para la detección de estrés a partir del texto de las redes sociales.
  • Superar las limitaciones de los métodos existentes en términos de tiempo de formación y utilización de las características.
  • Mejorar la precisión de la detección de tensiones mediante la integración y optimización de nuevos modelos.

Principales métodos:

  • Integración de técnicas avanzadas de representación de texto: FastText, Vectores globales para la representación de palabras (Glove), DeepMoji y XLNet.
  • Utilización de una convolución separable por profundidad con red residual (DSC-ResNet) para la detección precisa de tensiones.
  • Empleando el algoritmo de optimización de Chaotic Fennec Fox (CFFO) para el ajuste de hiperparámetros.

Principales resultados:

  • La técnica propuesta alcanzó una alta precisión del 98,42%.
  • La precisión, el recuerdo, la especificidad y la puntuación F1 fueron reportados en 97.58%, 98.12%, 98.28% y 98.38%, respectivamente.
  • El modelo demostró un rendimiento superior en comparación con las técnicas existentes.

Conclusiones:

  • La nueva integración de las representaciones de texto y DSC-ResNet ofrece una solución eficiente para la detección de estrés.
  • El método propuesto aborda efectivamente las limitaciones de los enfoques anteriores, proporcionando una alta precisión y rendimiento.
  • Esta técnica es prometedora para aplicaciones en el mundo real en el monitoreo de la salud mental a través del análisis de las redes sociales.